The development of a high-grade waterway network in the Upper Yangtze River will play a crucial role in promoting the economic and social development in the Chengdu-Chongqing region. Waterway improvement through underwater blasting to removal the obstructing rocks is still one of the most effective method in mountainous river management, but its impact on aquatic organisms, particularly on passively drifting fish eggs and larvae, has raised significant ecological concerns. Long-term observational data of drifting larvae in the Upper Yangtze River were analyzed, focusing on their temporal distribution patterns and spatial variations in species composition and abundance. The propagation mechanisms of underwater blasting shock waves were examined, and the primary factors affecting shock wave intensity were identified. Current research on blast-induced fish damage was reviewed, with special attention paid to the effects of shock waves on eggs and larvae. Based on the documented drift patterns of eggs and larvae and the characteristics of shock waves, comprehensive ecological protection measures were proposed. These measures included temporal and spatial avoidance strategies, optimized blasting techniques, and the use of bubble curtain technology. These findings provide valuable insights for achieving sustainable development that balances waterway construction with ecological conservation in the Upper Yangtze River.

The efficient utilization of electromagnetic spectrum resources has become a significant concern in the domain of wireless communications, with EMSM(electromagnetic spectrum map) playing a crucial role in visually representing spectrum usage within a specific task area and providing valuable support for the optimization of wireless networks. To address the challenges associated with generating fine-grained EMSMs under conditions of complex scenes and limited spatial point monitoring data, an improved DRN(deep residual network) model, ES-AFB(enhanced with a spatial attention feature block), was proposed. This model drew inspiration from image super-resolution techniques and leveraged the strong spatial characteristics of EMSMs to design a deep residual network capable of extracting the correlation and spectral features of EMSMs. The enhanced spatial attention feature block was utilized to mine the intrinsic implicit spatial features of coarse-grained EMSMs. Subsequently, the data size was reconfigured through the network’s multilayer up-sampling module, enabling the achievement of a more effective fine-grained image restoration. This approach allows for the generation of high-quality fine-grained EMSMs using limited coarse-grained monitoring data. The effectiveness of the algorithm is validated through simulation experiments, with the root-mean-square error of the EMSMs generated from actual data being found to be no more than 3%.

The data-driven approach of machine learning enables the intelligent construction of TBM(tunnel boring machines), which is crucial for optimizing the tunneling process, improving the safety of tunneling and reducing labor costs. In order to solve the problems of excessive noise, redundant parameters and difficult effective feature extraction in TBM operation data, a data-driven machine learning method was used to mine the complex machine-soil interaction contained in the data and realize the classification and prediction of TBM surrounding rock mass. First, for the large amount of operational data generated during TBM tunneling, the KDE (kernel density estimation) method was used to extract features from typical tunneling parameter curves, and the maximum probability of the key operating parameters during stable tunneling stage of TBM is obtained. Then, based on the actual TBM operation data, an integrated learning algorithm for surrounding rock classification stacking was proposed. The algorithm is further optimized through k-fold cross-validation, and the complex relationships in the data are mined by using the two-layer learning framework of base classifier and meta-classifier. Finally, a data set of 5 868 TBM segments was used to verify the effectiveness of the proposed algorithm. The results show that the average F₁ of the four-classification problem is 0.705, and the average F₁ of the two-classification problem is 0.797, which are better than the four selected base classifiers.

To solve the engineering problem of unclear standards and strong subjective experience when shield tunneling drivers set excavation parameters, which makes it difficult to control the shield tunneling attitude, an intelligent prediction model for shield tunneling attitude that considers the comprehensive effect of geological conditions, tunnel structure, and excavation parameters was proposed. Firstly, AWPSO (adaptive inertia weight particle swarm optimization) algorithm was established. Then, a shield attitude prediction model was constructed by combining GRU (gated recurrent unit) neural network, where the AWPSO algorithm was used to determine the optimal combination of hyperparameters in the GRU neural network. Finally, a case study was conducted to verify the on-site monitoring data between Zhangjiang Station and Resort Station on the Shanghai Suburban Railway Airport Connection Line. The results indicate that the proposed shield tunneling attitude prediction model based on AWPSO-GRU has high reliability and engineering practicality, which can provide reference and basis for setting construction parameters during shield tunneling.

Under the overarching vision of Healthy China, the imperative to investigate the design of health-oriented streets has gained paramount importance, aligning with the humanistic and sustainable evolution of urban landscapes. Addressing the limitations of existing health street evaluation methodologies marked by intricate indices, misalignment with the current state of China’s streetscapes, and a dearth of quantitative scrutiny, exploratory factor analysis was employed to distill latent variables. Through a structured approach encompassing health questionnaire analysis, structural equation modeling, and the quantification of health determinants, the research localizes health parameters and constructs a robust, quantifiable evaluation framework for street health. The analysis uncovers that four latent variables demonstrating significant positive correlations with street health outcomes, listed in descending order of influence magnitude: street quality improvement, accessible transportation provision, vibrant block development, and healthy environment promotion. The structural equation model-based quantitative analysis of street health elements furnishes scientific and empirical underpinnings for the development of superior health-conscious urban blocks. This methodological advancement not only refines the precision of street design geared towards health but also elevates the living standards of residents, thereby contributing to the realization of Healthy China’s aspirations.

Filling phase change capsules in a container to form a packed bed heat storage unit is a typical applica-tion of phase change capsules. Phase change capsules are usually stacked in a specific layout in the packed bed flow channel. Studying the heat storage and release characteristics of a single phase change capsule in a packed bed flow channel can help optimize the design of a medium-temperature phase change heat storage system. Therefore, a two-dimensional packed bed numerical model of phase change cap-sules was established. The heat transfer and flow characteristics of the external heat transfer fluid flowing through the phase change capsules in the direction of gravity, counter gravity and vertical gravity were compared and studied. The effects of flow rate, temperature and capsule diameter on the melting process of phase change capsules were studied. The results show that the heat transfer rate of the windward side of the phase change capsule in the packed bed channel is faster. Due to the thermal resistance of the cavity air and the natural convection, the complete solidification time is the shortest when the heat transfer fluid flows countercurrently. Compared with the downstream flow, the complete solidification time of the up-stream flow is shortened by 8.9%. When the diameter of the phase change capsule is 12 mm, the melting speed of the phase change capsule with PTFE as the wall material in the center is 1.45% slower than that of the 304 stainless steel phase change capsule, and the average heat storage rate is 1.5% lower. The melting rate of the phase change capsule with modified PTFE as the wall material cavity in the center is 6.9% faster than that of the 304 stainless steel phase change capsule, and the average heat storage rate is 5.8% higher. Increasing the HTF inlet velocity and temperature can increase the average heat storage rate of the phase change capsule and shorten the melting time of the phase change capsule. The heat storage and release characteristics of the capsule have important guiding significance for the design optimization and practical application of the capsule monomer and the medium temperature phase change heat storage system.

Due to the negative impact of abandoned powdered clay on land waste and pollution, it is beneficial to improve the powdered clay and use it for backfill in engineering construction.The effects of lignin fiber content and cement content on the unconfined compressive strength of silty clay excavated from a tunnel along the Yangtze River in Hangzhou,Zhejiang Province were studied. The formation mechanism of the compressive strength was analyzed. Finally,the pore microstructure of the sample was quantitatively analyzed by SEM experiment. The results show that the compressive strength reached the maximum value when the lignin fiber content is 4%,and the unconfined compressive strength was greater than that when the lignin fiber content is 2%, 6% and 8%. No matter how much lignin fiber content is,the unconfined compressive strength q'_u increases gradually with the increase of cement content. With the addition of lignin fiber,the average diameter of pores gradually concentrated in the range of particle size less than 1 μm,and the proportion of pores 1~2 μm and 2~4 μm increased with the increase of cement content. With the increase of lignin fiber and cement content,pore abundance mainly concentrated in the range of 0.2~0.5.With the addition of lignin fiber,the particle abundance mainly concentrated in the range of 0.1~0.6,and the particle distribution showed a “mountain” pattern with the addition of cement.

In order to study the effect of different scanning strategies on laser deposition of nickel-based alloy matrix, the process of deposition of IN718 alloy powder on IN718 alloy matrix under four different scanning strategies was numerically simulated by ABAQUS software, and tested under the same conditions. The heat source verification results show that the heat source model is accurate and effective, and the numerical simulation process accords with the actual deposition process. The analysis results of temperature field, stress field and deformation field show that the thermal influence of different side scanning is less than that of same side scanning and reciprocating scanning is lower than that of unidirectional scanning, and the peak temperature is lower, thus the residual stress is lower, and the deformation degree of the matrix can be effectively reduced. The experimental results show that the numerical simulation process is accurate and effective. It is concluded that the residual stress value and deformation can be reduced effectively by using the reciprocating scanning method on different sides.

In order to do a good job of emergency management in the airport sector, strengthen the construction of the emergency response system, and improve the emergency response capability, a decision support method based on hybrid reasoning was proposed for the disposal of airport emergencies. Firstly, the ontology model of airport emergencies was constructed by abstracting the emergencies and the disposal process in the airport based on the actual scenarios and official documents. Secondly, the hybrid reasoning combining rule-based reasoning and case-based reasoning was introduced for case retrieval, and case representation was performed for the constructed ontology model to construct a case database. Lastly, the retrieval results are corrected using a feature weighting algorithm for attribute trade-offs, and the attribute parameters were adjusted using a neural network-based weight parameter optimization strategy. The advantages of the Bert+LSTM combination in this task scenario were verified by comparing it with commonly used deep learning models, and the final example proves that when an emergency occurs, the model can focus on the emergency itself, refer to historical cases and disposal standards, and obtain a structured data that comprehensively describes the information and disposal measures of the emergency, which provides support for emergency disposal decision making.

Pressure drop plays an important effect on the performance of fiber filter-stick, and is determined by the materials and geometry structure. In order to develop a method to design and guide the production of the fiber filter, the multiple regression method uses the density of fiber tow, length, and circumference of the sticker as the argument to train the pressure drop model based on the production data. The fiber filter is modeled as multizone represented as the fiber tow and the forming paper. The flow dynamics in these zones are simulated based on the porous media model. The osmotic coefficients represent the pressure drop in the zone packed with the fiber tow. The simulation results show that the pressure drop is positively correlated with the length of the fiber rob, and the type of filter tow has a greater influence on the pressure drop than the circumference. For the design of the fiber filter, the regression model is first used to obtain the consumption of the fiber tow based on the design pressure drop. Then the simulation based on the porous media model is carried out to validate the prediction. If the error between the two methods is within 10%, this predicted fiber stick can be produced. By analyzing the production data and the prediction from models, it is concluded that the method proposed in this work is sufficient to direct the design and production.